No-Reference Quality Metric of Contrast-Distorted Images Based on Information Maximization

Gu, Ke; Wang, Lin; Zhai, Guangtao; Yang, Xiaokang; Zhang, Wenjun; Chen, Chang Wen

doi:10.1109/tcyb.2016.2575544

Cited by 298 publications

(101 citation statements)

References 33 publications

(37 reference statements)

Supporting

Mentioning

101

Contrasting

Order By: Relevance

“…The second category of models includes a pair of highly advanced models, namely, No Reference Image Quality Metric for Contrast distortion (NIQMC) [45] and Blind Image Quality Measure of Enhanced images (BIQME) [46]. These two studies focused specifically on the quality assessment of images with varying contrast to guide automatic image enhancement [47].…”

Section: The Methods Considered For Comparisonmentioning

confidence: 99%

PM2.5 Estimation Based on Image Analysis

Li¹,

Shan²,

Wang³

2020

KSII TIIS

View full text Add to dashboard Cite

For the severe haze situation in the Beijing-Tianjin-Hebei region, conventional fine particulate matter (PM2.5) concentration prediction methods based on pollutant data face problems such as incomplete data, which may lead to poor prediction performance. Therefore, this paper proposes a method of predicting the PM2.5 concentration based on image analysis technology that combines image data, which can reflect the original weather conditions, with currently popular machine learning methods. First, based on local parameter estimation, autoregressive (AR) model analysis and local estimation of the increase in image blur, we extract features from the weather images using an approach inspired by free energy and a no-reference robust metric model. Next, we compare the coefficient energy and contrast difference of each pixel in the AR model and then use the percentages to calculate the image sharpness to derive the overall mass fraction. Furthermore, the results are compared. The relationship between residual value and PM2.5 concentration is fitted by generalized Gauss distribution (GGD) model. Finally, nonlinear mapping is performed via the wavelet neural network (WNN) method to obtain the PM2.5 concentration. Experimental results obtained on real data show that the proposed method offers an improved prediction accuracy and lower root mean square error (RMSE).

show abstract

Section: The Methods Considered For Comparisonmentioning

confidence: 99%

PM2.5 Estimation Based on Image Analysis

Li¹,

Shan²,

Wang³

2020

KSII TIIS

View full text Add to dashboard Cite

show abstract

“…Discrete entropy represents the amount of information in an image. NIQMC assesses the quality of contrast-distorted images by considering both local and global information [41]. Table 2.…”

Section: A) Simulation Conditionsmentioning

confidence: 99%

Hue-correction scheme considering CIEDE2000 for color-image enhancement including deep-learning-based algorithms

Kinoshita

Kiya

2020

SIP

View full text Add to dashboard Cite

In this paper, we propose a novel hue-correction scheme for color-image-enhancement algorithms including deep-learning-based ones. Although hue-correction schemes for color-image enhancement have already been proposed, there are no schemes that can both perfectly remove perceptual hue-distortion on the basis of CIEDE2000 and be applicable to any image-enhancement algorithms. In contrast, the proposed scheme can perfectly remove hue distortion caused by any image-enhancement algorithm such as deep-learning-based ones on the basis of CIEDE2000. Furthermore, the use of a gamut-mapping method in the proposed scheme enables us to compress a color gamut into an output RGB color gamut, without hue changes. Experimental results show that the proposed scheme can completely correct hue distortion caused by image-enhancement algorithms while maintaining the performance of the algorithms and ensuring the color gamut of output images.

show abstract

“…Despite that the results were close to RIQMC results, these results depended on one database only which is CCIID2014. In [23], Gu et al presented a no-reference image quality assessment metric for contrast-distorted images. In the metric, they searched for local details.…”

Section: Literature Reviewmentioning

confidence: 99%